Streaming Elements for FPGA Signal and Image Processing Accelerators

Field-programmable gate array (FPGA) devices boast abundant resources with which custom accelerator components for signal, image, and data processing may be realized; however, realizing high-performance, low-cost accelerators currently demands manual register transfer level design. Software-programmable soft processors have been proposed as a way to reduce this design burden, but they are unable to support performance and cost comparable to custom circuits. This paper proposes a new soft processing approach for FPGA that promises to overcome this barrier. A high-performance, fine-grained streaming processor, known as a streaming accelerator element, is proposed, which realizes accelerators as large-scale custom multicore networks. By adopting a streaming execution approach with advanced program control and memory addressing capabilities, typical program inefficiencies can be almost completely eliminated to enable performance and cost, which are unprecedented among software-programmable solutions. When used to realize accelerators for fast Fourier transform, motion estimation, matrix multiplication, and sobel edge detection, it is shown how the proposed architecture enables real-time performance and with performance and cost comparable with hand-crafted custom circuit accelerators and up to two orders of magnitude beyond existing soft processors.